Implementation details. The method proposed in this paper is mainly implemented on the Pytorch framework. For a fair comparison, we apply the same network structure in each experiment. We utilize ResNet50 pre-trained on ImageNet without the final fully connected layer as the feature extractor. We adopt all the labeled data in the source domain and all the unlabeled data in the target domain. We apply the SGD optimizer with a momentum of 0.9, the batch size is 32, and the dimension of the bottleneck layer is set to 256. We adopt the learning rate annealing strategy as [1]}: the learning rate is adjusted by \(\eta _p=\eta _0(1+\alpha p)^{-\beta }\) , where \(p\)  is the training progress changing from 0 to 1, and \(\eta _0=0.01\) , \(\alpha =10\) , \(\beta =0.75\)  are optimized by the importance-weighted cross-validation [2]}. In the testing phase, we mainly choose the more stable teacher model for testing.
